Real-Time Motion Planning for Aerial Videography With Dynamic Obstacle Avoidance and Viewpoint Optimization

Nägeli, Tobias; Alonso–Mora, Javier; Domahidi, Alexander; Rus, Daniela; Hilliges, Otmar

doi:10.1109/lra.2017.2665693

Cited by 142 publications

(183 citation statements)

References 22 publications

Supporting

Mentioning

178

Contrasting

Order By: Relevance

“…This laptop controls the position of the drones at a high, and approximately constant, frequency of 20 Hz. We implement a slightly modified version of the Model Predictive Controller introduced by Nägeli et al (2017a) and extended to multiple drones by Nägeli et al (2017b), where we remove all the cost terms for videography. This controller minimizes the deviation from the assigned position of the drone in the formation, subject to collision avoidance, state and input constraints.…”

Section: Implementation Detailsmentioning

confidence: 99%

“…The NMPC by Nägeli et al (2017b) accounts for the internal constraints of the Parrot Bebop 2 (e.g., maximal vertical and horizontal velocities, maximal roll and pitch angles). The limits are described in the documentation of the Parrot SDK.…”

Section: Quadrotor Hardwarementioning

confidence: 99%

“…Multi-robot systems will be ubiquitous to perform many tasks, such as surveillance (Schwager et al 2011), inspection (Suzuki et al 2000), factory automation (Alonso-Mora et al 2015a), logistics (Wurman et al 2008) or cinematography (Nägeli et al 2017b). While some of these problems require team navigation in a rigid pattern, other scenarios, such as cooperative manipulation of deformable objects or transportation of cable-suspended loads, allow for more flexibility, yet requiring certain level of coordination.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Distributed multi-robot formation control in dynamic environments

et al. 2018

Self Cite

View full text Add to dashboard Cite

This paper presents a distributed method for formation control of a homogeneous team of aerial or ground mobile robots navigating in environments with static and dynamic obstacles. Each robot in the team has a finite communication and visibility radius and shares information with its neighbors to coordinate. Our approach leverages both constrained optimization and multi-robot consensus to compute the parameters of the multi-robot formation. This ensures that the robots make progress and avoid collisions with static and moving obstacles. In particular, via distributed consensus, the robots compute (a) the convex hull of the robot positions, (b) the desired direction of movement and (c) a large convex region embedded in the four dimensional position-time free space. The robots then compute, via sequential convex programming, the locally optimal parameters for the formation to remain within the convex neighborhood of the robots. The method allows for reconfiguration. Each robot then navigates towards its assigned position in the target collision-free formation via an individual controller that accounts for its dynamics. This approach is efficient and scalable with the number of robots. We present an extensive evaluation of the communication requirements and verify the method in simulations with up to sixteen quadrotors. Lastly, we present experiments with four real quadrotors flying in formation in an environment with one moving human.

show abstract

Section: Implementation Detailsmentioning

confidence: 99%

Section: Quadrotor Hardwarementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Distributed multi-robot formation control in dynamic environments

et al. 2018

Self Cite

View full text Add to dashboard Cite

show abstract

“…When it comes to HRT task scheduling, hybrid methods are always employed. The hybrid approaches utilize design-time computations to identify a timing constraint satisfying allocation at runtime [36,37].…”

Section: Multi-robot Task Schedulingmentioning

confidence: 99%

CPS-Based Smart Warehouse for Industry 4.0: A Survey of the Underlying Technologies

et al. 2018

View full text Add to dashboard Cite

This paper discusses how the state-of-the-art techniques in cyber-physical systems facilitate building smart warehouses to achieve the promising vision of industry 4.0. We focus on four significant issues when applying CPS techniques in smart warehouses. First, efficient CPS data collection: when limited communication bandwidth meets numerous CPS devices, we need to make more effort to study efficient wireless communication scheduling strategies. Second, accurate and robust localization: localization is the basis for many fundamental operations in smart warehouses, but still needs to be improved from various aspects like accuracy and robustness. Third, human activity recognition: human activity recognition can be applied in human-computer interaction for remote machine operations. Fourth, multi-robot collaboration: smart robots will take the place of humans to accomplish most tasks particularly in a harsh environment, and smart and fully-distributed robot collaborating algorithms should be investigated. Finally, we point out some challenging issues in the future CPS-based smart warehouse, which could open some new research directions.

show abstract

“…In the visual servoing literature, to the best of our knowledge, the real-time predictive controllers used for a visual tracking task are [15], [16]. Although [15] formulated an c 2020 IEEE. Personal use of this material is permitted.…”

Section: Introductionmentioning

confidence: 99%

Aggressive Perception-Aware Navigation Using Deep Optical Flow Dynamics and PixelMPC

Lee

Gibson

Theodorou

2020

IEEE Robot. Autom. Lett.

View full text Add to dashboard Cite

Recently, vision-based control has gained traction by leveraging the power of machine learning. In this work, we couple a model predictive control (MPC) framework to a visual pipeline. We introduce deep optical flow (DOF) dynamics, which is a combination of optical flow and robot dynamics. Using the DOF dynamics, MPC explicitly incorporates the predicted movement of relevant pixels into the planned trajectory of a robot. Our implementation of DOF is memory-efficient, data-efficient, and computationally cheap so that it can be computed in real-time for use in an MPC framework. The suggested Pixel Model Predictive Control (PixelMPC) algorithm controls the robot to accomplish a high-speed racing task while maintaining visibility of the important features (gates). This improves the reliability of vision-based estimators for localization and can eventually lead to safe autonomous flight. The proposed algorithm is tested in a photorealistic simulation with a high-speed drone racing task. Supplementary video: https://youtu.be/NzL2YRcOh I

show abstract

Real-Time Motion Planning for Aerial Videography With Dynamic Obstacle Avoidance and Viewpoint Optimization

Cited by 142 publications

References 22 publications

Distributed multi-robot formation control in dynamic environments

Distributed multi-robot formation control in dynamic environments

CPS-Based Smart Warehouse for Industry 4.0: A Survey of the Underlying Technologies

Aggressive Perception-Aware Navigation Using Deep Optical Flow Dynamics and PixelMPC

Contact Info

Product

Resources

About